AI-Driven Analysis and Prediction of IT Service Management Performance Metrics Using Multi-Dataset Evaluation

Abstract

Artificial Intelligence (AI) is one of the key technologies that can enhance the efficiency of IT Service Management (ITSM) ecosystems. Traditional ITSM systems often have manual workflows for incident management, ticket prioritization and service monitoring that can introduce delays and impact service quality. Mean Time to Resolution (MTTR) stands out as one of the key operational metrics commonly employed to assess the effectiveness of incident management operations. This research suggests an artificial-intelligence-based framework to evaluate and predict the performance of ITSM operations through multi-dataset evaluation and optimization based on simulation. The framework incorporates dataset preprocessing, calculation of MTTR, AI-driven simulation, performance comparative and rendering visualization. There were several available ITSM datasets providing incident lifecycle and service management information which were gathered and analyzed with Python-based analytical tools. The operational timestamps were used for calculation of baseline MTTR, followed by optimization with AI-assisted simulation models considering adjustable reduction parameters and certain controlled variability. Comparative analysis of operational performance between baseline and AI assisted shows that the MTTR can be measured with a reduction in operational performance efficiency in various datasets, consequently, improved incident handling process efficiency, which is measurable. The suggested framework also features interactive visualization and simulation tools created with Streamlit, which can aid real-time operational analysis and decision-making. The results of the experiments have shown that AI optimization can prove to be a valuable tool to decrease incident resolution time and improve the performance of ITSM systems. The research provides an operational, analyse-based, practical and simulation-oriented analytical framework, which has been enhanced with comparative analysis and intelligent visualization techniques for the performance improvement of ITSM beyond the conventional prediction models.